Table 3. New surgical scheduling system: summary of challenges and enablers mapped to the CFIR domains.

CFIR domain	Construct definition	Main challenge themes	Main enabler themes
Innovation	Innovation design (how the system is designed and presented)	Limited granularity in urgency coding; inconsistent waitlist practices; inaccurate or incomplete case-time prediction inputs; manual, fragmented scheduling and equipment workflows.	Opportunity to redesign scheduling around more granular data, clearer workflows and automation of routine tasks (eg, equipment selection, bed needs).
	Innovation complexity (perceived complexity and number of steps)	Concern about notification overload, overflagging of complex cases and additional steps that may increase workload.	None explicitly reported; implied need to streamline notifications and preserve familiar features.
	Relative advantage (advantages over current system)	Current system perceived as ‘good enough’; uncertainty that a new system can improve throughput under resource constraints; risk that change effort may not yield meaningful gains.	Anticipated improvements in efficiency, throughput, workload, reporting, resource allocation and revenue if key features (eg, automatic tracking, better data) are realised.
	Evidence base (perceived strength of evidence)	Scepticism about automation and ML based on mixed experiences in other hospitals; desire to see local proof of concept.	Confidence drawn from literature and other sectors’ use of ML; perception that greater automation is necessary and timely.
	Adaptability (fit with local needs and constraints)	Uncertainty about how the system will handle site-specific protocols, resource constraints, teaching demands and patient-specific factors; dependence on variable human data entry.	Adaptability viewed as both essential and feasible if the system supports local tailoring and override capability.
Inner setting	Access to knowledge and information (training/support)	Risk of insufficient or poorly timed training; variable digital skills; potential early system glitches; slow IT support.	Strong appetite for role-specific, multimodal training and responsive support.
	Structural characteristics—work infrastructure	Unclear future roles and responsibilities for schedulers; concern that workload may increase without added staffing.	None explicitly reported.
	Structural characteristics—IT infrastructure	Hybrid paper/electronic systems, inconsistent data quality and uncertainty related to upcoming hip replacement.	None explicitly reported; implied that better integration could improve access and performance.
	Available resources—funding	Uncertain funding for implementation and ongoing oversight; possible need for additional position(s).	None explicitly reported.
	Tension for change (need for change)	Some feel the current system works well; concern that change could worsen performance.	Others see clear room for improvement and would welcome enhancements.
	Incentive systems	Surgeons may resist if assistants’ workload increases, especially when they fund these roles.	None explicitly reported.
	Mission alignment	Past efficiency initiatives seen as undermining work–life balance and teaching; fear this may recur.	Holland Centre seen as open to innovation and strongly focused on scheduling and efficiency.
	Compatibility and relative priority	Concerns about missing features and misfit with existing workflows; some see low priority if current system already optimises OR time.	Holland Centre viewed as an ideal starting site due to case homogeneity, high utilisation and perceived potential for even modest efficiency gains.
Implementation	Doing (approach to rollout)	Risk of top-down implementation without early user input, leading to late discovery of problems.	Preference for phased rollout, pilots and iterative testing with early and ongoing KU engagement.
	Reflecting and evaluating	Unclear plans for monitoring, sustaining use and validating predicted times.	Recognition of the need for ongoing measurement and feedback to refine the system.
Individuals	Motivation	Anticipated resistance and scepticism due to past consultant-led ‘efficiency’ projects and perceived loss of control; uncertain buy-in from surgeons and anaesthetists.	Many KUs are pro-change and supportive if engaged, able to retain some flexibility and see clear rationale and benefits.
	Capability	Potential variability in technology comfort.	Overall confidence that KUs have, or can easily acquire, the skills needed to use the new system.

CFIR, Consolidated Framework for Implementation Research; IT, information technology; KU, knowledge user; ML, machine learning; OR, operating room.